Predicting fatigue and psychophysiological test performance from speech for safety critical environments

Frontiers in Bioengineering and Biotechnology

Iya eWhiteley¹  Svetlana eAndreeva²  Oleg eRyumin²  Khan Richard Baykaner³  Mark eHuckvale³ 

[1] Centre for Space Medicine;Gagarin Cosmonaut Training Centre;University College London;

关键词: Fatigue;    Memory;    Reaction Time;    Speech;    bioinformatics;    machine learning;   

DOI  :  10.3389/fbioe.2015.00124

来源: DOAJ

【 摘 要 】

Automatic systems for estimating operator fatigue have application in safety-critical environments. A system which could estimate level of fatigue from speech would have application in domains where operators engage in regular verbal communication as part of their duties. Previous studies on the prediction of fatigue from speech have been limited because of their reliance on subjective ratings and because they lack comparison to other methods for assessing fatigue. In this paper we present an analysis of voice recordings and psychophysiological test scores collected from seven aerospace personnel during a training task in which they remained awake for 60 hours. We show that voice features and test scores are affected by both the total time spent awake and the time position within each subject’s circadian cycle. However, we show that time spent awake and time of day information are poor predictors of the test results; while voice features can give good predictions of the psychophysiological test scores and sleep latency. Mean absolute errors of prediction are possible within about 17.5% for sleep latency and 5-12% for test scores. We discuss the implications for the use of voice as a means to monitor the effects of fatigue on cognitive performance in practical applications.

【 授权许可】

Unknown